A web map of the Dulux colours of New Zealand

Build a web map of the Dulux colours of New Zealand

Libraries

We need a bunch of these. First jsonlite for processing the colours data pulled from the website.

library(jsonlite) # for processing the JSON files of colours

Next, a bunch of data munging packages from the tidyverse.

library(plyr)     # rbind.fill is super useful for ragged data with missing rows
library(dplyr)  
library(magrittr)
library(tibble)
library(stringr) 

And finally R spatial packages.

library(sf)
library(tmap)
library(tmaptools) # geocoding

Getting the colours

See this website for what this is all about. Prior to building this notebook I had a poke around on the website to figure out where the colour details were to be found.

colour_groups <- c("blues", "browns", "greens", "greys", "oranges",
                  "purples", "reds", "whites-neutrals", "yellows")
base_url <- "https://www.dulux.co.nz/content/duluxnz/home/colour/all-colours.categorycolour.json/all-colours/"
colour_sets <- str_c(base_url, colour_groups)

The loop below, steps through the group names, retrieves the relevant JSON file from the web, writes it out locally, and parses the key information into a data table.

df_colours <- NULL
for (group in colour_groups) {
  source <- str_c(base_url, group)
  file_name <- str_c(group, ".json")
  json <- fromJSON(source, flatten = TRUE)
  write_json(json, file_name)
  the_colours <- rbind.fill(json$categoryColours$masterColour.colours)
  if (is.null(df_colours)) {
    df_colours <- the_colours
  } else {
    df_colours <- bind_rows(df_colours, the_colours)
  }
  Sys.sleep(0.5)  
}
write.csv(df_colours, "dulux-colours-raw.csv", row.names = FALSE)

We can check what we got

df_colours <- read.csv("dulux-colours-raw.csv")
head(df_colours)

##       id red green blue      baseId           name woodType coats
## 1 149253 205   210  206 vivid_white Pukaki Quarter     None    NA
## 2 149254 220   240  242 vivid_white      Canoe Bay     None    NA
## 3 149255 226   240  245 vivid_white      Mt Dobson     None    NA
## 4 149256 220   230  235 vivid_white        Raetihi     None    NA
## 5 149257 217   219  223 vivid_white   Taiaroa Head     None    NA
## 6 149258 180   200  219 vivid_white   Gulf Harbour     None    NA

Tidying up the names

There are paints with various modifiers as suffixes to specify slightly different shades of particular colours.

paint_modifiers <- c("Half", "Quarter", "Double")

A tidy pipeline is a nice way to clean this up. There are other ways (like writing a function, but it’s nice to show it using just built-in tidy operations).

df_colours_tidied <- df_colours %>%
  ## remove some columns we won't be needing
  select(-id, -baseId, -woodType, -coats) %>%
  ## separate the name components, filling from the left with NAs if <5
  separate(name, c("p1", "p2", "p3", "p4", "p5"), sep = " ", 
           remove = FALSE, fill = "left") %>%
  ## replace any NAs with an empty string
  mutate(p1 = str_replace_na(p1, ""),
         p2 = str_replace_na(p2, ""),
         p3 = str_replace_na(p3, ""),
         p4 = str_replace_na(p4, "")) %>%
  ## if p5 is a paint modifiers, then recompose name from p1:p4 else from p1:p5
  ## similarly keep modifier where it exists
  mutate(placename = if_else(p5 %in% paint_modifiers, 
                             str_trim(str_c(p1, p2, p3, p4, sep = " ")), 
                             str_trim(str_c(p1, p2, p3, p4, p5, sep = " "))),
         modifier = if_else(p5 %in% paint_modifiers, 
                            p5, "")) %>%
  ## throw away variables we no longer and reorder
  select(name, placename, modifier, red, green, blue)

# save it so we have it for later
write.csv(df_colours_tidied, "dulux-colours.csv", row.names = FALSE)

Build the spatial dataset

Add x and y columns to our data for the coordinates - note that we re-load from the saved file so as not to repeat hitting the Dulux website.

df_colours_tidied <- read.csv("dulux-colours.csv")
df_colours_tidied %<>%
  mutate(x = 0, y = 0)

Go through all the unique names, and append as many x y coordinates as we have space for (due to the modifiers) from the geocoding results.

In general best not to re-run this (it takes a good 10 minutes and it’s not good to repeatedly re-geocode and hit the OSM server).

for (placename in unique(df_colours_tidied$placename)) {
  address <- str_c(placename, "New Zealand", sep = ", ")
  geocode <- geocode_OSM(address, as.data.frame = TRUE, return.first.only = FALSE)
  num_geocodes <- nrow(geocode)
  matching_rows <- which(df_colours_tidied$placename == placename)
  for (i in 1:length(matching_rows)) {
    if (!is.null(geocode)) {
      if (num_geocodes >= i) {
        df_colours_tidied[matching_rows[i], ]$x <- geocode$lon[i] 
        df_colours_tidied[matching_rows[i], ]$y <- geocode$lat[i] 
      }  
    }
  }
  Sys.sleep(0.5) # so as not to over-tax the geocoder
}

Do another tidy up removing anything that didn’t get geocoded and also remove some nuisance locations! Save again to another file.

df_colours_tidied <- df_colours_tidied %>%
  filter(x != 0 & y != 0 & 
         placename != "Chatham Islands" & placename != "Passage Rock" & 
         placename != "Auckland Islands" & placename != "Cossack Rock")

write.csv(df_colours_tidied, "dulux-colours-xy.csv", row.names = FALSE)

Making a map

Get the dataset we made earlier.

dulux_colours <- read.csv("dulux-colours-xy.csv")

Now make it into an sf point dataset

dulux_colours_sf <- st_as_sf(dulux_colours, coords = c("x", "y"), crs = 4326) %>%
  st_transform(2193) %>%
  mutate(rgb = rgb(red / 255, 
                   green / 255, 
                   blue/ 255))

st_write(dulux_colours_sf, "dulux-colours-pts.gpkg", delete_dsn = TRUE)

## Deleting source `dulux-colours-pts.gpkg' using driver `GPKG'
## Writing layer `dulux-colours-pts' to data source 
##   `dulux-colours-pts.gpkg' using driver `GPKG'
## Writing 903 features with 8 fields and geometry type Point.

And at last a map!

tmap_mode("view")

## tmap mode set to interactive viewing

tm_shape(dulux_colours_sf) + 
  tm_dots()

Better yet, Voronois

Now make up voronois and clip to NZ.

dulux_colours_vor <- dulux_colours_sf %>%
  st_union() %>%
  st_voronoi() %>%
  st_cast() %>%
  st_as_sf() %>%
  st_join(dulux_colours_sf, left = FALSE) %>%
  st_intersection(st_read("nz.gpkg")) 

## Reading layer `nz' from data source 
##   `/home/osullid3/Documents/code/dulux-colours/nz.gpkg' using driver `GPKG'
## Simple feature collection with 1 feature and 0 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1089972 ymin: 4748123 xmax: 2091863 ymax: 6223160
## Projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000

## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries

st_write(dulux_colours_vor, "dulux-colours-vor.gpkg", delete_dsn = TRUE)

## Deleting source `dulux-colours-vor.gpkg' using driver `GPKG'
## Writing layer `dulux-colours-vor' to data source 
##   `dulux-colours-vor.gpkg' using driver `GPKG'
## Writing 901 features with 8 fields and geometry type Unknown (any).

And map it

tm_shape(dulux_colours_vor) + 
  tm_polygons(col = "rgb", id = "placename", alpha = 0.75, border.col = "grey", lwd = 0.2) +
  tm_basemap("Esri.WorldTopoMap")